
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis'/'Sensitivity')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)


from platform import python_version 
print(python_version())

3.8.8


import PV_ICE
PV_ICE.__version__

'v0.2.0+486.g25106de.dirty'


MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')


#load in a baseline and materials for modification
sim1 = PV_ICE.Simulation(name='sim1', path=testfolder)

sim1.createScenario(name='PV_ICE', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['PV_ICE'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\Sensitivity
Baseline folder directed to default:  C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\baselines


timeshift = 2022-1995


#life sensitivity
#mod_reliability_t50, mod_reliability_t90, mod_degradation, mod_lifetime

sim1.createScenario(name='life_high', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['life_high'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim1.modifyScenario('life_high', 'mod_degradation', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']-0.1, start_year=2022) #standard current degrdation
#T50
sim1.modifyScenario('life_high', 'mod_reliability_t50',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']+10, start_year=2022)
#t90
sim1.modifyScenario('life_high', 'mod_reliability_t90',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']+10, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('life_high', 'mod_lifetime',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']+10, start_year=2022) #project lifetime of 25 years

#--------------------------------------------------------------------------------------

#life low
sim1.createScenario(name='life_low', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['life_low'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim1.modifyScenario('life_low', 'mod_degradation', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']+0.1, start_year=2022) #standard current degrdation
#T50
sim1.modifyScenario('life_low', 'mod_reliability_t50',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']-10, start_year=2022)
#t90
sim1.modifyScenario('life_low', 'mod_reliability_t90',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']-10, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('life_low', 'mod_lifetime',
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']-10, start_year=2022) #project lifetime of 25 years


sim1.createScenario(name='eff_high', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['eff_high'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
sim1.modifyScenario('eff_high', 'mod_eff', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']+5, start_year=2022) #

#-------------------------------------------------------------------------------------------------------

sim1.createScenario(name='eff_high_bifi', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['eff_high_bifi'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
sim1.modifyScenario('eff_high_bifi', 'mod_eff', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']+5, start_year=2022) #

#------------------------------------------------------------------------------------------------------

sim1.createScenario(name='eff_low', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['eff_low'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
sim1.modifyScenario('eff_low', 'mod_eff', 
                    sim1.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']-5, start_year=2022) #


mod_circ_vars = ['mod_EOL_collection_eff', 'mod_EOL_pg4_recycled', 'mod_EOL_pb4_recycled']

mod_alt_paths = ['mod_EOL_pg0_resell','mod_EOL_pg1_landfill','mod_EOL_pg2_stored','mod_EOL_pg3_reMFG',
                 'mod_EOL_reMFG_yield','mod_EOL_sp_reMFG_recycle',
                 'mod_EOL_pb1_landfill','mod_EOL_pb2_stored','mod_EOL_pb3_reMFG']

mat_circ_vars = ['mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff', 'mat_MFG_scrap_Recycled_into_HQ',
                 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 'mat_PG4_Recycling_target', 'mat_Recycling_yield',
                 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG']


sim1.createScenario(name='circ_high', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['circ_high'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
for var in range(0,len(mod_alt_paths)):
    sim1.modifyScenario('circ_high', mod_alt_paths[var], 0.0, start_year=2022) #set non recycle to 0   

sim1.modifyScenario('circ_high', 'mod_EOL_collection_eff',100.0, start_year=2022) #collect everything
    
for var in range(0,len(mod_circ_vars)):
    sim1.modifyScenario('circ_high', mod_circ_vars[var], 100.0, start_year=2022) #set recycle paths to 100%

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim1.scenario['circ_high'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],100.0, start_year=2022) #
        #sim1.scenario['circ_high'].modifyMaterials(MATERIALS[mat], 'mat_PG4_Recycling_target',100.0, start_year=2022) #

#--------------------------------------------------------------------------------------------------------
        
sim1.createScenario(name='circ_mid', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['circ_mid'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
for var in range(0,len(mod_alt_paths)):
    sim1.modifyScenario('circ_mid', mod_alt_paths[var], 0.0, start_year=2022) #set non recycle to 0   

sim1.modifyScenario('circ_mid', 'mod_EOL_collection_eff',100.0, start_year=2022) #collect everything
    
for var in range(0,len(mod_circ_vars)):
    sim1.modifyScenario('circ_mid', mod_circ_vars[var], 25.0, start_year=2022) #set recycle paths to 25%

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim1.scenario['circ_mid'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],100.0, start_year=2022) #
        #sim1.scenario['circ_mid'].modifyMaterials(MATERIALS[mat], 'mat_PG4_Recycling_target',25.0, start_year=2022) #
#-----------------------------------------------------------------------------------------------------------

sim1.createScenario(name='circ_low', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['circ_low'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#sim1.scenMod_noCircularity(scenarios='circ_low') #sets all years to 0
    
for var in range(0,len(mod_circ_vars)):
    sim1.modifyScenario('circ_low', mod_circ_vars[var],0.0, start_year=2022) #set recycle to 0
    
for var in range(0,len(mod_alt_paths)):
    sim1.modifyScenario('circ_low', mod_alt_paths[var], 0.0, start_year=2022) #set non recycle to 0   

sim1.modifyScenario('circ_low', 'mod_EOL_collection_eff',0.0, start_year=2022) #collect nothing

sim1.modifyScenario('circ_low','mod_EOL_pb1_landfill',100.0,start_year=2022) #landfill up just in case
sim1.modifyScenario('circ_low','mod_EOL_pg1_landfill',100.0,start_year=2022)

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim1.scenario['circ_low'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],0.0, start_year=2022) #


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim1.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE glass : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silicon : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silver : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE copper : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  life_high
life_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
life_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
life_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
life_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
life_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
life_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
life_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
life_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  life_low
life_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
life_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
life_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
life_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
life_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
life_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
life_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
life_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_high
eff_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_high_bifi
eff_high_bifi glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_high_bifi backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  eff_low
eff_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
eff_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_high
circ_high glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_high backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_mid
circ_mid glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_mid silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_mid silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_mid aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_mid copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_mid encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_mid backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_mid backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_low
circ_low glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_low backsheet : Data trimmed for Mass, years now encompass  2000  to  2100


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)
global_projection.iloc[-1,:]

#fig, ax1 = plt.subplots()

#ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
#ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
#ax1.set_ylim(0,90)
#ax1.set_xlim(2000,2100)
#ax2 = ax1.twinx()
#ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
#ax2.set_ylabel('Annual Installations [TW]')
#ax2.set_ylim(0,5)
#plt.show()

#fig.savefig('energyresults-deployment.png', dpi=300, bbox_inches='tight')

World_cum              85966000.0
World_annual_[MWdc]      219320.0
Name: 2100, dtype: float64


#deployment projection for all scenarios
sim1.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)


scennames_labels = sim1.scenario.keys()
scennames_labels

dict_keys(['PV_ICE', 'life_high', 'life_low', 'eff_high', 'eff_high_bifi', 'eff_low', 'circ_high', 'circ_mid', 'circ_low'])


bifiFactors = {'PV_ICE':0.0,
               'life_high':0.0,
               'life_low': 0.0,
               'eff_high': 0.0,
               'eff_high_bifi': 0.9,
               'eff_low': 0.0,
               'circ_high':0.0,
               'circ_mid':0.0,
               'circ_low': 0.0
              } 

#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
idx_temp = pd.RangeIndex(start=2000,stop=2101,step=1) #create the index
df_temp = pd.DataFrame(index=idx_temp, columns=['bifi'], dtype=float)
bifiPathDict={}

for f in bifiFactors.keys(): #loop over module types
    bifi = df_temp.copy() #copy of df
    bifi['bifi'] = bifiFactors[f] #assign column
    bifipath = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi.to_csv(path_or_buf=bifipath, index_label='year') #create file
    bifiPathDict[str(f)] = bifipath
    #append bifi path to dict? or list?


#CALCULATE MASS FLOWs
#13 scenarios, 7 mats = 

for scen in sim1.scenario.keys(): #loop over scenarios
    sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_high
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_low
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high_bifi
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_low
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_high
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_high  =  2787030969.8975344  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_high  =  92130764.51066588  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_high  =  522204.79505684547  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_high  =  315960505.1026145  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_high  =  1904912.986497598  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_high  =  185857396.31027597  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_high  =  78240039.20964287  tonnes.
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_mid
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_mid  =  426233162.6874477  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_mid  =  14056687.844464416  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_mid  =  79082.69019301583  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_mid  =  48247896.22123377  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_mid  =  290035.9591884496  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_mid  =  28333066.983154226  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_mid  =  11899991.040827412  tonnes.
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_low
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


#sim1.calculateMassFlow()


ii_yearly, ii_cumu = sim1.aggregateResults() #have to do this to get auto plots
sim1.saveSimulation(customname='_EnergySensitivity_identicalinstalls')

:) Saved Input and Output Dataframes


effective_capacity = ii_yearly.filter(like='ActiveCapacity')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=sim1.scenario.keys())
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


cumu_installs = ii_cumu.filter(like='newInstalled')
total_installed = cumu_installs.loc[2100]
total_installed.index= scennames_labels

plt.bar(scennames_labels, total_installed/1e6)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Installs with Replacements')


cumu_virgin_module = ii_cumu.filter(like='VirginStock_Module')
virgin = cumu_virgin_module.loc[2100]
virgin.index= scennames_labels

plt.bar(scennames_labels, virgin/1e9)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
#plt.xticks(rotation=90)

Text(0, 0.5, 'Virgin Material Requirements\n[billion tonnes]')


sim1.scenario['eff_high'].dataIn_m


#plt.plot(sim1.scenario['eff_high'].dataOut_m['Yearly_Sum_Area_PathsBad'])
#plt.plot(sim1.scenario['eff_high'].dataOut_m['Cumulative_Active_Area'])
plt.plot(sim1.scenario['eff_high'].dataOut_m['Area']/1e10, label='eff_high')

#plt.plot(sim1.scenario['eff_high_bifi'].dataOut_m['Yearly_Sum_Area_PathsBad'])
#plt.plot(sim1.scenario['eff_high_bifi'].dataOut_m['Cumulative_Active_Area'])
plt.plot(sim1.scenario['eff_high_bifi'].dataOut_m['Area']/1e10, label='eff_high_bifi')

plt.plot(sim1.scenario['PV_ICE'].dataOut_m['Area']/1e10, label='PVICE')


plt.plot(sim1.scenario['eff_high_bifi'].dataIn_m['new_Installed_Capacity_[MW]']/1e6, label='eff_high_bifi_installs')
plt.plot(sim1.scenario['PV_ICE'].dataIn_m['new_Installed_Capacity_[MW]']/1e6, label='PVICE_installs')
plt.legend()

<matplotlib.legend.Legend at 0x1fc069ebac0>


UnderInstall_df = pd.DataFrame()


for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim1.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Effective_Capacity_[W]'][row])/1e6)  # MWATTS
        sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        UnderInstall_df.loc[row,scen] = Under_Installment #save the underinstallment as df
        #calculate flows for that scenario with it's bifi factor and modified weibull
        sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

sim1.calculateEnergyFlow()

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
life_high
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_high
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
life_low
>>>> Calculating Material Flows <<<<

Working on Scenario:  life_low
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
eff_high
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
eff_high_bifi
>>>> Calculating Material Flows <<<<

Working on Scenario:  eff_high_bifi
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1835082948.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


scenarios = sim1.scenario.keys()
materials = sim1.scenario['PV_ICE'].material.keys()

allenergy = pd.DataFrame()
energyGen = pd.DataFrame()
energyFuel = pd.DataFrame()
energy_mod = pd.DataFrame()
scenmatde = pd.DataFrame()
energy_mat = pd.DataFrame()
energy_demands = pd.DataFrame()

for scen in scenarios:
    # add the scen name as a prefix 
    energy_mod_scen = sim1.scenario[scen].dataOut_e.add_prefix(str(scen+'_'))
    #concat into one large df
    #energy_mod = pd.concat([energy_mod, energy_mod_scen], axis=1)
            
    #material level energy
    for mat in materials:
        # add the scen name as a prefix 
        energy_mat_scen = sim1.scenario[scen].material[mat].matdataOut_e.add_prefix(str(scen+'_'+mat+'_'))
        scenmatde = pd.concat([scenmatde,energy_mat_scen], axis=1)
        #concat into one large df
        #energy_mat = pd.concat([energy_mat, scenmatde], axis=1)
            
    #compile module and material energies into one df
    allenergy_scen_t = pd.concat([energy_mod_scen,scenmatde], axis=1) #df of mod and mat energy for scenario


for scen in scenarios:
     # add the scen name as a prefix \
    #energy_mod = pd.DataFrame()
    energy_mod = sim1.scenario[scen].dataOut_e.add_prefix(str(scen+'_'))
    #concat into one large df
    #energy_mod = pd.concat([energy_mod, scende], axis=1)
            
    #material level energy
    for mat in materials:
        # add the scen name as a prefix 
        #scenmatde = pd.DataFrame()
        energy_mat = sim1.scenario[scen].material[mat].matdataOut_e.add_prefix(str(scen+'_'+mat+'_'))
        scenmatde = pd.concat([scenmatde,energy_mat], axis=1)
        #concat into one large df
        #energy_mat = pd.concat([energy_mat, scenmatde], axis=1)
            
    #compile module and material energies into one df
    allenergy_scen = pd.concat([energy_mod,scenmatde], axis=1) #df of mod and mat energy for scenario
    
    
        #select df to sum the total demand
    energyGen_scen = allenergy_scen.filter(like='e_out_annual') #select all columns of energy generation
    energyFuel_scen = allenergy_scen.filter(like='_fuel') #select all columns of fuel attributable
    energy_demands_1 = allenergy_scen.loc[:,~allenergy_scen.columns.isin(energyGen_scen.columns)] #select all columns that are NOT energy generation, i.e. demands
    energy_demands_scen = energy_demands_1.loc[:,~energy_demands_1.columns.isin(energyFuel_scen.columns)] #select all columns that are NOT fuel (this avoids double counting)
    colname = str(scen+'_e_demand_total')
    energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
            
    allenergy = pd.concat([allenergy,allenergy_scen], axis=1)
    energyGen = pd.concat([energyGen,energyGen_scen], axis=1)
    energyFuel = pd.concat([energyFuel,energyFuel_scen], axis=1)
    energy_demands = pd.concat([energy_demands,energy_demands_scen], axis=1)

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\3251266812.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands_scen.loc[:,colname] = energy_demands_scen.sum(axis=1)


sim1.saveSimulation(customname='_EnergySensitivity_withreplacements')

#aggregate results - mass
cc_yearly, cc_cumu = sim1.aggregateResults() #have to do this to get auto plots
cc_yearly.to_csv(os.path.join(testfolder, 'cc_10scen_yearly.csv'))
cc_cumu.to_csv(os.path.join(testfolder, 'cc_10scen_cumu.csv'))

#aggregate results - energy
allenergy, energyGen, energy_demands = sim1.aggregateEnergyResults()
allenergy.to_csv(os.path.join(testfolder, 'cc_10scen_allenergy.csv'))
energyGen.to_csv(os.path.join(testfolder, 'cc_10scen_energyGen.csv'))
energy_demands.to_csv(os.path.join(testfolder, 'cc_10scen_energy_demands.csv'))

UnderInstall_df.to_csv(os.path.join(testfolder, 'cc_10scen_underInstalls.csv'))

:) Saved Input and Output Dataframes

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands


scennames_labels

dict_keys(['PV_ICE', 'life_high', 'life_low', 'eff_high', 'eff_high_bifi', 'eff_low', 'circ_high', 'circ_mid', 'circ_low'])


cumu_installs = cc_cumu.filter(like='newInstalled')
total_installed = cumu_installs.loc[2100]
total_installed.index= scennames_labels

plt.bar(scennames_labels, total_installed/1e6)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Installs with Replacements')


cumu_area_deployed = pd.DataFrame()

for scen in sim1.scenario.keys():
    area_deploy = sim1.scenario[scen].dataOut_m['Area'].cumsum()
    cumu_area_deployed = pd.concat([cumu_area_deployed,area_deploy], axis=1)
    
cumu_area_deployed.columns = sim1.scenario.keys()
cumu_area_deployed_total = cumu_area_deployed.iloc[-1,:]
#cumu_area_deployed

plt.bar(scennames_labels, cumu_area_deployed.iloc[-1,:]/1e12)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed Area [m2 1e12]')
plt.title('Cumulative Area Deployed with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Area Deployed with Replacements')


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
virgin = cumu_virgin_module.loc[2100]
virgin.index= scennames_labels

plt.bar(scennames_labels, virgin/1e9)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
#plt.xticks(rotation=90)

Text(0, 0.5, 'Virgin Material Requirements\n[billion tonnes]')


cumu_lifecycle_wastes = cc_cumu.filter(like='WasteAll_Module')
wastes = cumu_lifecycle_wastes.loc[2100]
wastes.index= scennames_labels

plt.bar(scennames_labels, wastes/1e9)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[billion tonnes]')

Text(0, 0.5, 'Lifecycle Wastes\n[billion tonnes]')


e_annual_sumDemands = energy_demands.filter(like='demand_total')
e_annual_sumDemands_cumu = e_annual_sumDemands.cumsum()
cumu_e_demands = e_annual_sumDemands_cumu.loc[2100]
cumu_e_demands.index= scennames_labels

plt.bar(scennames_labels, cumu_e_demands/1e12)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')

Text(0, 0.5, 'Cumulative Energy Demands\n[TWh]')


e_demands_eff_high = energy_demands.filter(like='eff_high')
e_demands_eff_high_cumu = e_demands_eff_high.cumsum()

e_demands_eff_high_cumu

#plt.bar(e_demands_eff_high_cumu.columns, e_demands_eff_high_cumu.iloc[-1,:])


energyGen_cumu = energyGen.cumsum()
energyGen_cumu.columns = e_annual_sumDemands_cumu.columns = scennames_labels
netEnergy_cumu = energyGen_cumu.loc[[2100]] - e_annual_sumDemands_cumu.loc[[2100]]
#netEnergy_cumu.index= scennames_labels

plt.bar(scennames_labels, netEnergy_cumu.loc[2100]/1e12)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')

Text(0, 0.5, 'Cumulative Net Energy [TWh]')


netEnergy_cumu_norm = netEnergy_cumu/netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall = netEnergy_cumu_norm-1

plt.bar(scennames_labels, netEnergy_cumu_norm_waterfall.loc[2100])
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)
plt.xticks(rotation=90)

([0, 1, 2, 3, 4, 5, 6, 7, 8],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


energyBalance_allyears = energyGen_cumu/e_annual_sumDemands_cumu
energyBalance = energyBalance_allyears.loc[2100]
energyBalance.index = scennames_labels

plt.bar(scennames_labels, energyBalance)
plt.title('Energy Balance')
plt.ylabel('Unitless')

Text(0, 0.5, 'Unitless')


discussTable = pd.concat([total_installed,cumu_area_deployed_total,virgin,wastes,
                          cumu_e_demands, netEnergy_cumu.loc[2100].T, energyBalance],
                         axis=1, keys=['replacements','area','virgin','wastes','energydemand','netenergy','energybalance'])
#discussTable.to_csv(os.path.join(testfolder,'discussiontable.csv'))
discussTable


discussTable_norm = (discussTable/discussTable.loc['PV_ICE'])*100-100
discussTable_norm


#load in a baseline and materials for modification
sim2 = PV_ICE.Simulation(name='sim1', path=testfolder)

sim2.createScenario(name='PV_ICE', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['PV_ICE'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\Sensitivity
Baseline folder directed to default:  C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\baselines


mod_circ_vars = ['mod_EOL_collection_eff', 'mod_EOL_pg4_recycled', 'mod_EOL_pb4_recycled']
mat_circ_vars = ['mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff', 'mat_MFG_scrap_Recycled_into_HQ',
                 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 'mat_PG4_Recycling_target', 'mat_Recycling_yield',
                 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG']


#Life+Eff

sim2.createScenario(name='good_eff_life', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['good_eff_life'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim2.modifyScenario('good_eff_life', 'mod_degradation', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']-0.1, start_year=2022) #standard current degrdation
#T50
sim2.modifyScenario('good_eff_life', 'mod_reliability_t50',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']+10, start_year=2022)
#t90
sim2.modifyScenario('good_eff_life', 'mod_reliability_t90',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']+10, start_year=2022) 
#Mod Project Lifetime
sim2.modifyScenario('good_eff_life', 'mod_lifetime',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']+10, start_year=2022) #

sim2.modifyScenario('good_eff_life', 'mod_eff', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']+5, start_year=2022) #


#Eff+Circ
sim2.createScenario(name='good_eff_circ', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['good_eff_circ'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

for var in range(0,len(mod_circ_vars)):
    sim2.modifyScenario('good_eff_circ', mod_circ_vars[var], 100.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim2.scenario['good_eff_circ'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],100.0, start_year=2022) #
        sim2.scenario['good_eff_circ'].modifyMaterials(MATERIALS[mat], 'mat_PG4_Recycling_target',25.0, start_year=2022) #

sim2.modifyScenario('good_eff_circ', 'mod_eff', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']+5, start_year=2022) #


#Life+Circ

sim2.createScenario(name='good_life_circ', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['good_life_circ'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim2.modifyScenario('good_life_circ', 'mod_degradation', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']-0.1, start_year=2022) #standard current degrdation
#T50
sim2.modifyScenario('good_life_circ', 'mod_reliability_t50',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']+10, start_year=2022)
#t90
sim2.modifyScenario('good_life_circ', 'mod_reliability_t90',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']+10, start_year=2022) 
#Mod Project Lifetime
sim2.modifyScenario('good_life_circ', 'mod_lifetime',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']+10, start_year=2022) #

for var in range(0,len(mod_circ_vars)):
    sim2.modifyScenario('good_life_circ', mod_circ_vars[var], 100.0, start_year=2022) #

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim2.scenario['good_life_circ'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],100.0, start_year=2022) #
        sim2.scenario['good_life_circ'].modifyMaterials(MATERIALS[mat], 'mat_PG4_Recycling_target',25.0, start_year=2022) #


#life eff
sim2.createScenario(name='bad_life_eff', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['bad_life_eff'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
#Lifetime and Degradation
#degradation rate:
sim2.modifyScenario('bad_life_eff', 'mod_degradation', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']+0.1, start_year=2022) #standard current degrdation
#T50
sim2.modifyScenario('bad_life_eff', 'mod_reliability_t50',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']-10, start_year=2022)
#t90
sim2.modifyScenario('bad_life_eff', 'mod_reliability_t90',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']-10, start_year=2022) 
#Mod Project Lifetime
sim2.modifyScenario('bad_life_eff', 'mod_lifetime',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']-10, start_year=2022) #project lifetime of 25 years
#eff -5
sim2.modifyScenario('bad_life_eff', 'mod_eff', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']-5, start_year=2022) #


#eff circ
sim2.createScenario(name='bad_eff_circ', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['bad_eff_circ'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

sim2.modifyScenario('bad_eff_circ', 'mod_eff', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_eff']-5, start_year=2022) #

#sim2.scenMod_noCircularity(scenarios='bad_life_eff')

for var in range(0,len(mod_circ_vars)):
    sim2.modifyScenario('bad_eff_circ', mod_circ_vars[var],0.0, start_year=2022) #

sim2.modifyScenario('bad_eff_circ','mod_EOL_pb1_landfill',100.0,start_year=2022)
sim2.modifyScenario('bad_eff_circ','mod_EOL_pg1_landfill',100.0,start_year=2022)
    
for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim2.scenario['bad_eff_circ'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],0.0, start_year=2022) #


#life circ
sim2.createScenario(name='bad_life_circ', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim2.scenario['bad_life_circ'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

#degradation rate:
sim2.modifyScenario('bad_life_circ', 'mod_degradation', 
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_degradation']+0.1, start_year=2022) #standard current degrdation
#T50
sim2.modifyScenario('bad_life_circ', 'mod_reliability_t50',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t50']-10, start_year=2022)
#t90
sim2.modifyScenario('bad_life_circ', 'mod_reliability_t90',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_reliability_t90']-10, start_year=2022) 
#Mod Project Lifetime
sim2.modifyScenario('bad_life_circ', 'mod_lifetime',
                    sim2.scenario['PV_ICE'].dataIn_m.loc[timeshift:,'mod_lifetime']-10, start_year=2022)
    
#0 circ
#sim2.scenMod_noCircularity(scenarios='bad_life_circ') #sets all years to 0.0

for var in range(0,len(mod_circ_vars)):
    sim2.modifyScenario('bad_life_circ', mod_circ_vars[var],0.0, start_year=2022) #

sim2.modifyScenario('bad_life_circ','mod_EOL_pb1_landfill',100.0,start_year=2022)
sim2.modifyScenario('bad_life_circ','mod_EOL_pg1_landfill',100.0,start_year=2022)

for mat in range (0, len(MATERIALS)):
    for mvar in range(0,len(mat_circ_vars)):
        sim2.scenario['bad_life_circ'].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar],0.0, start_year=2022) #


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim2.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE glass : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silicon : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silver : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE copper : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  good_eff_life
good_eff_life glass : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life silicon : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life silver : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life copper : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_life backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  good_eff_circ
good_eff_circ glass : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ silver : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ copper : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
good_eff_circ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  good_life_circ
good_life_circ glass : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ silver : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ copper : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
good_life_circ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  bad_life_eff
bad_life_eff glass : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff silicon : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff silver : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff copper : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_eff backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  bad_eff_circ
bad_eff_circ glass : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ silver : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ copper : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
bad_eff_circ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  bad_life_circ
bad_life_circ glass : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ silver : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ copper : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
bad_life_circ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)
global_projection.iloc[-1,:]

#fig, ax1 = plt.subplots()

#ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
#ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
#ax1.set_ylim(0,90)
#ax1.set_xlim(2000,2100)
#ax2 = ax1.twinx()
#ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
#ax2.set_ylabel('Annual Installations [TW]')
#ax2.set_ylim(0,5)
#plt.show()

#fig.savefig('energyresults-deployment.png', dpi=300, bbox_inches='tight')

World_cum              85966000.0
World_annual_[MWdc]      219320.0
Name: 2100, dtype: float64


#deployment projection for all scenarios
sim2.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)


sim2.scenario.keys()

dict_keys(['PV_ICE', 'good_eff_life', 'good_eff_circ', 'good_life_circ', 'bad_life_eff', 'bad_eff_circ', 'bad_life_circ'])


bifiFactors = {'PV_ICE':0.0,
               'good_eff_life':0.9, 
               'good_eff_circ': 0.9,
               'good_life_circ': 0.0,
               'bad_life_eff': 0.0,
               'bad_eff_circ':0.0,
               'bad_life_circ': 0.0
              } 

#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
idx_temp = pd.RangeIndex(start=2000,stop=2101,step=1) #create the index
df_temp = pd.DataFrame(index=idx_temp, columns=['bifi'], dtype=float)
bifiPathDict={}

for f in bifiFactors.keys(): #loop over module types
    bifi = df_temp.copy() #copy of df
    bifi['bifi'] = bifiFactors[f] #assign column
    bifipath = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi.to_csv(path_or_buf=bifipath, index_label='year') #create file
    bifiPathDict[str(f)] = bifipath
    #append bifi path to dict? or list?


#CALCULATE MASS FLOW= 

for scen in sim2.scenario.keys(): #loop over scenarios
    sim2.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_eff_life
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_eff_circ
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  good_eff_circ  =  352582643.1300519  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  good_eff_circ  =  11627565.469418474  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  good_eff_circ  =  65412.32638255321  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  good_eff_circ  =  39910337.86350069  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  good_eff_circ  =  239908.727872841  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  good_eff_circ  =  23437294.579840444  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  good_eff_circ  =  9842645.002890877  tonnes.
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_life_circ
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  good_life_circ  =  492415492.96914035  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  good_life_circ  =  16233488.131774146  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  good_life_circ  =  91212.2117321412  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  good_life_circ  =  55722105.60383685  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  good_life_circ  =  334772.7185564503  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  good_life_circ  =  32734035.070287634  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  good_life_circ  =  13714479.678606484  tonnes.
>>>> Calculating Material Flows <<<<

Working on Scenario:  bad_life_eff
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  bad_eff_circ
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  bad_life_circ
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


#sim1.calculateMassFlow()


ii_yearly2, ii_cumu2 = sim2.aggregateResults() #have to do this to get auto plots
sim2.saveSimulation(customname='_EnergySensitivity-goodbad_identicalinstalls')

:) Saved Input and Output Dataframes


effective_capacity = ii_yearly2.filter(like='ActiveCapacity')

plt.plot(ii_cumu2['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')

plt.plot(effective_capacity/1e6, label=sim2.scenario.keys())

plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


## Calculate Flows: Capacity Compensation


UnderInstall_df = pd.DataFrame()


for row in range (0,len(sim2.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim2.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim2.scenario[scen].dataOut_m['Effective_Capacity_[W]'][row])/1e6)  # MWATTS
        sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        UnderInstall_df.loc[row,scen] = Under_Installment #save the underinstallment as df
        #calculate flows for that scenario with it's bifi factor and modified weibull
        sim2.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

sim2.calculateEnergyFlow()

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
good_eff_life
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_eff_life
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
good_eff_circ
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_eff_circ
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  good_eff_circ  =  352582643.1300519  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  good_eff_circ  =  11627565.469418474  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  good_eff_circ  =  65412.32638255321  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  good_eff_circ  =  39910337.86350069  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  good_eff_circ  =  239908.727872841  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  good_eff_circ  =  23437294.579840444  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  good_eff_circ  =  9842645.002890877  tonnes.
good_life_circ
>>>> Calculating Material Flows <<<<

Working on Scenario:  good_life_circ
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  good_life_circ  =  492415492.96914035  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  good_life_circ  =  16233488.131774146  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  good_life_circ  =  91212.2117321412  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  good_life_circ  =  55722105.60383685  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  good_life_circ  =  334772.7185564503  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  good_life_circ  =  32734035.070287634  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  good_life_circ  =  13714479.678606484  tonnes.
bad_life_eff
>>>> Calculating Material Flows <<<<

Working on Scenario:  bad_life_eff
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2724\1670677626.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim2.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


sim2.saveSimulation(customname='_EnergySensitivity_withreplacements')

#aggregate results - mass
cc_yearly2, cc_cumu2 = sim2.aggregateResults() #have to do this to get auto plots
cc_yearly2.to_csv(os.path.join(testfolder, 'cc_10scen_yearly.csv'))
cc_cumu2.to_csv(os.path.join(testfolder, 'cc_10scen_cumu.csv'))

#aggregate results - energy
allenergy2, energyGen2, energy_demands2 = sim2.aggregateEnergyResults()
allenergy2.to_csv(os.path.join(testfolder, 'cc_10scen_allenergy.csv'))
energyGen2.to_csv(os.path.join(testfolder, 'cc_10scen_energyGen.csv'))
energy_demands2.to_csv(os.path.join(testfolder, 'cc_10scen_energy_demands.csv'))

#UnderInstall_df.to_csv(os.path.join(testfolder, 'cc_10scen_underInstalls.csv'))

:) Saved Input and Output Dataframes

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands


scennames_labels2=sim2.scenario.keys()


cumu_installs2 = cc_cumu2.filter(like='newInstalled')
total_installed2 = cumu_installs2.loc[2100]
total_installed2.index= scennames_labels2

plt.bar(scennames_labels2, total_installed2/1e6)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Installs with Replacements')


cumu_area_deployed2 = pd.DataFrame()

for scen in sim2.scenario.keys():
    area_deploy = sim2.scenario[scen].dataOut_m['Area'].cumsum()
    cumu_area_deployed2 = pd.concat([cumu_area_deployed2,area_deploy], axis=1)
    
cumu_area_deployed2.columns = sim2.scenario.keys()
cumu_area_deployed2_total = cumu_area_deployed2.iloc[-1,:]
#cumu_area_deployed

plt.bar(scennames_labels2, cumu_area_deployed2.iloc[-1,:]/1e12)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed Area [m2 1e12]')
plt.title('Cumulative Area Deployed with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Area Deployed with Replacements')


cumu_virgin_module2 = cc_cumu2.filter(like='VirginStock_Module')
virgin2 = cumu_virgin_module2.loc[2100]
virgin2.index= scennames_labels2

plt.bar(scennames_labels2, virgin2/1e9)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
plt.xticks(rotation=45)

([0, 1, 2, 3, 4, 5, 6],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


cumu_lifecycle_wastes2 = cc_cumu2.filter(like='WasteAll_Module')
wastes2 = cumu_lifecycle_wastes2.loc[2100]
wastes2.index= scennames_labels2

plt.bar(scennames_labels2, wastes2/1e9)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[billion tonnes]')
plt.xticks(rotation=45)

([0, 1, 2, 3, 4, 5, 6],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


e_annual_sumDemands2 = energy_demands2.filter(like='demand_total')
e_annual_sumDemands_cumu2 = e_annual_sumDemands2.cumsum()
cumu_e_demands2 = e_annual_sumDemands_cumu2.loc[2100]
cumu_e_demands2.index= scennames_labels2

plt.bar(scennames_labels2, cumu_e_demands2/1e12)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')

Text(0, 0.5, 'Cumulative Energy Demands\n[TWh]')


energyGen_cumu2 = energyGen2.cumsum()
energyGen_cumu2.columns = e_annual_sumDemands_cumu2.columns = scennames_labels2
netEnergy_cumu2 = energyGen_cumu2.loc[[2100]] - e_annual_sumDemands_cumu2.loc[[2100]]
#netEnergy_cumu.index= scennames_labels

plt.bar(scennames_labels2, netEnergy_cumu2.loc[2100]/1e12)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')

Text(0, 0.5, 'Cumulative Net Energy [TWh]')


netEnergy_cumu_norm2 = netEnergy_cumu2/netEnergy_cumu2.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall2 = netEnergy_cumu_norm2-1

plt.bar(scennames_labels2, netEnergy_cumu_norm_waterfall2.loc[2100])
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)
plt.xticks(rotation=90)

([0, 1, 2, 3, 4, 5, 6],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


energyBalance_allyears2 = energyGen_cumu2/e_annual_sumDemands_cumu2
energyBalance2 = energyBalance_allyears2.loc[2100]
energyBalance2.index = scennames_labels2

plt.bar(scennames_labels2, energyBalance2)
plt.title('Energy Balance')
plt.ylabel('Unitless')

Text(0, 0.5, 'Unitless')


discussTable2 = pd.concat([total_installed2,cumu_area_deployed2_total,virgin2,wastes2,
                           cumu_e_demands2, netEnergy_cumu2.loc[2100].T, energyBalance2],
                         axis=1, keys=['replacements','area','virgin','wastes','energydemand','netenergy','energybalance'])
#discussTable.to_csv(os.path.join(testfolder,'discussiontable.csv'))
discussTable2


discussTable_all = pd.concat([discussTable,discussTable2], axis=0)
discussTable_all


discussTable_norm = (discussTable_all/discussTable.loc['PV_ICE'])*100-100
discussTable_norm


graph_order = ['life_high','life_low','good_eff_life','bad_life_eff','eff_high','eff_high_bifi','eff_low','good_eff_circ',
               'bad_eff_circ','circ_high','circ_low','good_life_circ','bad_life_circ']


#load in a baseline and materials for modification
sim3 = PV_ICE.Simulation(name='sim3', path=testfolder)

sim3.createScenario(name='PV_ICE', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim3.scenario['PV_ICE'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\Sensitivity
Baseline folder directed to default:  C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\baselines


mod_circ_vars = ['mod_EOL_pg4_recycled', 'mod_EOL_pb4_recycled']

mod_alt_paths = ['mod_EOL_pg0_resell','mod_EOL_pg1_landfill','mod_EOL_pg2_stored','mod_EOL_pg3_reMFG',
                 'mod_EOL_reMFG_yield','mod_EOL_sp_reMFG_recycle',
                 'mod_EOL_pb1_landfill','mod_EOL_pb2_stored','mod_EOL_pb3_reMFG']

mat_circ_vars = ['mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff', 'mat_MFG_scrap_Recycled_into_HQ',
                 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 'mat_PG4_Recycling_target', 'mat_Recycling_yield',
                 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG']


#range of circularity / closed loop vs downcycling
rrates = pd.Series(range(0,101,10))
for r in range(0,len(rrates)): print(rrates[r])

0
10
20
30
40
50
60
70
80
90
100


#closed loop at various rates
for r in range(0,len(rrates)):
    scenname = 'circ_CL_'+str(rrates[r])
    sim3.createScenario(name=scenname, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim3.scenario[scenname].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
    for var in range(0,len(mod_alt_paths)):
        sim3.modifyScenario(scenname, mod_alt_paths[var], 0.0, start_year=2022) #set non recycle to 0
    
    sim3.modifyScenario(scenname, 'mod_EOL_collection_eff',100.0, start_year=2022) #collect everything
    
    for var in range(0,len(mod_circ_vars)):
        sim3.modifyScenario(scenname, mod_circ_vars[var], rrates[r], start_year=2022) #recycle modules at the rate

    for mat in range (0, len(MATERIALS)):
        for mvar in range(0,len(mat_circ_vars)):
            sim3.scenario[scenname].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar], 100.0, start_year=2022) #100% recycle HQ path and yield
            #sim3.scenario[scenname].modifyMaterials(MATERIALS[mat], 'mat_PG4_Recycling_target', rrates[r], start_year=2022) #


#open loop at various rates
for r in range(0,len(rrates)):
    scenname = 'circ_OL_'+str(rrates[r])
    sim3.createScenario(name=scenname, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim3.scenario[scenname].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)
    
    for var in range(0,len(mod_alt_paths)):
        sim3.modifyScenario(scenname, mod_alt_paths[var], 0.0, start_year=2022) #set non recycle to 0
        
    sim3.modifyScenario(scenname, 'mod_EOL_collection_eff',100.0, start_year=2022) #collect everything
    
    for var in range(0,len(mod_circ_vars)):
        sim3.modifyScenario(scenname, mod_circ_vars[var], rrates[r], start_year=2022) #recycle modules at the rate

    for mat in range (0, len(MATERIALS)):
        for mvar in range(0,len(mat_circ_vars)):
            sim3.scenario[scenname].modifyMaterials(MATERIALS[mat], mat_circ_vars[mvar], 100.0, start_year=2022) # 100% recycle HQ path and yield
            #sim3.scenario[scenname].modifyMaterials(MATERIALS[mat], 'mat_PG4_Recycling_target', rrates[r], start_year=2022) #
            sim3.scenario[scenname].modifyMaterials(MATERIALS[mat], 'mat_MFG_scrap_Recycled_into_HQ', 0.0, start_year=2022) #Open loop
            sim3.scenario[scenname].modifyMaterials(MATERIALS[mat], 'mat_EOL_RecycledHQ_Reused4MFG', 0.0, start_year=2022) #Open loop


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim3.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE glass : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silicon : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silver : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE copper : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_0
circ_CL_0 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_0 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_0 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_0 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_0 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_0 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_0 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_0 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_10
circ_CL_10 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_10 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_10 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_10 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_10 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_10 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_10 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_10 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_20
circ_CL_20 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_20 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_20 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_20 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_20 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_20 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_20 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_20 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_30
circ_CL_30 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_30 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_30 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_30 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_30 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_30 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_30 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_30 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_40
circ_CL_40 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_40 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_40 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_40 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_40 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_40 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_40 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_40 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_50
circ_CL_50 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_50 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_50 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_50 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_50 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_50 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_50 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_50 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_60
circ_CL_60 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_60 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_60 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_60 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_60 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_60 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_60 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_60 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_70
circ_CL_70 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_70 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_70 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_70 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_70 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_70 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_70 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_70 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_80
circ_CL_80 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_80 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_80 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_80 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_80 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_80 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_80 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_80 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_90
circ_CL_90 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_90 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_90 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_90 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_90 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_90 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_90 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_90 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_CL_100
circ_CL_100 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_100 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_100 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_100 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_100 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_100 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_100 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_CL_100 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_0
circ_OL_0 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_0 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_0 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_0 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_0 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_0 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_0 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_0 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_10
circ_OL_10 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_10 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_10 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_10 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_10 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_10 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_10 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_10 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_20
circ_OL_20 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_20 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_20 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_20 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_20 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_20 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_20 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_20 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_30
circ_OL_30 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_30 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_30 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_30 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_30 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_30 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_30 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_30 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_40
circ_OL_40 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_40 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_40 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_40 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_40 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_40 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_40 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_40 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_50
circ_OL_50 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_50 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_50 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_50 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_50 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_50 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_50 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_50 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_60
circ_OL_60 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_60 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_60 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_60 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_60 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_60 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_60 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_60 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_70
circ_OL_70 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_70 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_70 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_70 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_70 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_70 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_70 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_70 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_80
circ_OL_80 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_80 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_80 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_80 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_80 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_80 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_80 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_80 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_90
circ_OL_90 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_90 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_90 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_90 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_90 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_90 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_90 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_90 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  circ_OL_100
circ_OL_100 glass : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_100 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_100 silver : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_100 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_100 copper : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_100 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_100 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
circ_OL_100 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)
global_projection.iloc[-1,:]

#fig, ax1 = plt.subplots()

#ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
#ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
#ax1.set_ylim(0,90)
#ax1.set_xlim(2000,2100)
#ax2 = ax1.twinx()
#ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
#ax2.set_ylabel('Annual Installations [TW]')
#ax2.set_ylim(0,5)
#plt.show()

#fig.savefig('energyresults-deployment.png', dpi=300, bbox_inches='tight')

World_cum              85966000.0
World_annual_[MWdc]      219320.0
Name: 2100, dtype: float64


#deployment projection for all scenarios
sim3.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)


sim3.calculateMassFlow()

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_CL_0
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_CL_10
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_CL_20
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_20  =  271707812.1406806  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_20  =  8966531.127012994  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_20  =  50564.64897692408  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_20  =  30773905.64463607  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_20  =  185189.97356997622  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_20  =  18059609.777788714  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_20  =  7619659.725812049  tonnes.
Working on Scenario:  circ_CL_30
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_30  =  580758513.2342132  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_30  =  19146844.56191587  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_30  =  107600.7314091076  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_30  =  65721886.79783118  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_30  =  394881.9448069221  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_30  =  38606524.18851982  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_30  =  16180322.355842829  tonnes.
Working on Scenario:  circ_CL_40
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_40  =  889809214.327747  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_40  =  29327157.996818677  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_40  =  165440.00686850527  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_40  =  100669867.95102637  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_40  =  605187.275731472  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_40  =  59153438.59925088  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_40  =  24849261.0780108  tonnes.
Working on Scenario:  circ_CL_50
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_50  =  1201241697.236229  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_50  =  39664519.98678356  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_50  =  224490.6353431577  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_50  =  136081455.86607113  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_50  =  819930.530903125  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_50  =  79987275.22633837  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_50  =  33684889.47250623  tonnes.
Working on Scenario:  circ_CL_60
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_60  =  1516714180.410585  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_60  =  50112393.00871647  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_60  =  283718.6634325451  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_60  =  171880846.6307254  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_60  =  1036146.3690535728  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_60  =  101071559.92187954  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_60  =  42565832.923453495  tonnes.
Working on Scenario:  circ_CL_70
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_70  =  1833548222.2197533  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_70  =  60606938.05154895  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_70  =  342990.8845836029  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_70  =  207858748.47799954  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_70  =  1252873.9033478543  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_70  =  122248040.58373827  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_70  =  51446776.37440069  tonnes.
Working on Scenario:  circ_CL_80
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_80  =  2151312686.060818  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_80  =  71101483.09438154  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_80  =  402474.12784421135  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_80  =  243858241.82510284  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_80  =  1469601.4376421338  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_80  =  143424521.24559745  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_80  =  60358010.90507804  tonnes.
Working on Scenario:  circ_CL_90
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_90  =  2469077149.9018927  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_90  =  81602984.70466155  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_90  =  462146.6814235276  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_90  =  279857735.1722062  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_90  =  1687047.5853848225  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_90  =  164613862.17420936  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_90  =  69293758.78940447  tonnes.
Working on Scenario:  circ_CL_100
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_100  =  2787030969.8975344  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_100  =  92130764.51066588  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_100  =  522204.79505684547  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_100  =  315960505.1026145  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_100  =  1904912.986497598  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_100  =  185857396.31027597  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_100  =  78240039.20964287  tonnes.
Working on Scenario:  circ_OL_0
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_10
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_20
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_30
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_40
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_50
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_60
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_70
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_80
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_90
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
Working on Scenario:  circ_OL_100
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


UnderInstall_df = pd.DataFrame()


for row in range (0,len(sim3.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim3.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim3.scenario[scen].dataOut_m['Effective_Capacity_[W]'][row])/1e6)  # MWATTS
        sim3.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        UnderInstall_df.loc[row,scen] = Under_Installment #save the underinstallment as df
        #calculate flows for that scenario with it's bifi factor and modified weibull
        sim3.calculateMassFlow(scenarios=[scen])

sim3.calculateEnergyFlow()

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3660\2775174709.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim3.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
circ_CL_0
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_CL_0
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3660\2775174709.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim3.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
circ_CL_10
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_CL_10
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3660\2775174709.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim3.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
circ_CL_20
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_CL_20
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3660\2775174709.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim3.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  circ_CL_20  =  271707812.1406806  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  circ_CL_20  =  8966531.127012994  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  circ_CL_20  =  50564.64897692408  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  circ_CL_20  =  30773905.64463607  tonnes.
==> Working on Material :  copper
Recycled surplus End of Sim for Mat  copper  Scenario  circ_CL_20  =  185189.97356997622  tonnes.
==> Working on Material :  encapsulant
Recycled surplus End of Sim for Mat  encapsulant  Scenario  circ_CL_20  =  18059609.777788714  tonnes.
==> Working on Material :  backsheet
Recycled surplus End of Sim for Mat  backsheet  Scenario  circ_CL_20  =  7619659.725812049  tonnes.
circ_CL_30
>>>> Calculating Material Flows <<<<

Working on Scenario:  circ_CL_30
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3660\2775174709.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim3.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


sim3.saveSimulation(customname='_EnergySensitivity_withreplacements')

#aggregate results - mass
cc_yearly3, cc_cumu3 = sim3.aggregateResults() #have to do this to get auto plots
cc_yearly3.to_csv(os.path.join(testfolder, 'cc_10scen_yearly3.csv'))
cc_cumu3.to_csv(os.path.join(testfolder, 'cc_10scen_cumu3.csv'))

#aggregate results - energy
allenergy3, energyGen3, energy_demands3 = sim3.aggregateEnergyResults()
allenergy3.to_csv(os.path.join(testfolder, 'cc_10scen_allenergy3.csv'))
energyGen3.to_csv(os.path.join(testfolder, 'cc_10scen_energyGen3.csv'))
energy_demands3.to_csv(os.path.join(testfolder, 'cc_10scen_energy_demands3.csv'))

#UnderInstall_df.to_csv(os.path.join(testfolder, 'cc_10scen_underInstalls.csv'))

:) Saved Input and Output Dataframes

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2236: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands


scennames_labels3=sim3.scenario.keys()


cumu_installs3 = cc_cumu3.filter(like='newInstalled')
total_installed3 = cumu_installs3.loc[2100]
total_installed3.index= scennames_labels3

plt.bar(scennames_labels3, total_installed3/1e6)
#plt.xticks(rotation=90)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Installs with Replacements')


cumu_area_deployed3 = pd.DataFrame()

for scen in sim3.scenario.keys():
    area_deploy = sim3.scenario[scen].dataOut_m['Area'].cumsum()
    cumu_area_deployed3 = pd.concat([cumu_area_deployed3,area_deploy], axis=1)
    
cumu_area_deployed3.columns = sim3.scenario.keys()
cumu_area_deployed3_total = cumu_area_deployed3.iloc[-1,:]
#cumu_area_deployed

plt.bar(scennames_labels3, cumu_area_deployed3.iloc[-1,:]/1e12)
plt.xticks(rotation=45)
plt.ylabel('Cumulative installed Area [m2 1e12]')
plt.title('Cumulative Area Deployed with Replacements')
#plt.ylim(0,410)

Text(0.5, 1.0, 'Cumulative Area Deployed with Replacements')


cumu_virgin_module3 = cc_cumu3.filter(like='VirginStock_Module')
virgin3 = cumu_virgin_module3.loc[2100]
virgin3.index= scennames_labels3

plt.bar(scennames_labels3, virgin3/1e9)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
#plt.xticks(rotation=90)

Text(0, 0.5, 'Virgin Material Requirements\n[billion tonnes]')


cumu_lifecycle_wastes3 = cc_cumu3.filter(like='WasteAll_Module')
wastes3 = cumu_lifecycle_wastes3.loc[2100]
wastes3.index= scennames_labels3

plt.bar(scennames_labels3, wastes3/1e9)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[billion tonnes]')

Text(0, 0.5, 'Lifecycle Wastes\n[billion tonnes]')


e_annual_sumDemands3 = energy_demands3.filter(like='demand_total')
e_annual_sumDemands_cumu3 = e_annual_sumDemands3.cumsum()
cumu_e_demands3 = e_annual_sumDemands_cumu3.loc[2100]
cumu_e_demands3.index= scennames_labels3

plt.bar(scennames_labels3, cumu_e_demands3/1e12)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')

Text(0, 0.5, 'Cumulative Energy Demands\n[TWh]')


cumu_e_demands3_norm = cumu_e_demands3/cumu_e_demands3.loc['PV_ICE']-1
plt.bar(scennames_labels3, cumu_e_demands3_norm)
plt.title('Energy Demands - Normalized')
plt.ylabel('Unitless')
plt.xticks(rotation=90)

([0,
  1,
  2,
  3,
  4,
  5,
  6,
  7,
  8,
  9,
  10,
  11,
  12,
  13,
  14,
  15,
  16,
  17,
  18,
  19,
  20,
  21,
  22],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


energyGen_cumu3 = energyGen3.cumsum()
energyGen_cumu3.columns = e_annual_sumDemands_cumu3.columns = scennames_labels3
netEnergy_cumu3 = energyGen_cumu3.loc[[2100]] - e_annual_sumDemands_cumu3.loc[[2100]]
#netEnergy_cumu.index= scennames_labels

plt.bar(scennames_labels3, netEnergy_cumu3.loc[2100]/1e12)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')

Text(0, 0.5, 'Cumulative Net Energy [TWh]')


netEnergy_cumu_norm3 = netEnergy_cumu3/netEnergy_cumu3.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall3 = netEnergy_cumu_norm3-1

plt.bar(scennames_labels3, netEnergy_cumu_norm_waterfall3.loc[2100])
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)
plt.xticks(rotation=90)

([0,
  1,
  2,
  3,
  4,
  5,
  6,
  7,
  8,
  9,
  10,
  11,
  12,
  13,
  14,
  15,
  16,
  17,
  18,
  19,
  20,
  21,
  22],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


energyBalance_allyears3 = energyGen_cumu3/e_annual_sumDemands_cumu3
energyBalance3 = energyBalance_allyears3.loc[2100]
energyBalance3.index = scennames_labels3

plt.bar(scennames_labels3, energyBalance3)
plt.title('Energy Balance')
plt.ylabel('Unitless')

Text(0, 0.5, 'Unitless')


energyBalance3_norm = energyBalance3/energyBalance3.loc['PV_ICE']-1
plt.bar(scennames_labels3, energyBalance3_norm)
plt.title('Energy Balance - Normalized')
plt.ylabel('Unitless')
plt.xticks(rotation=90)

([0,
  1,
  2,
  3,
  4,
  5,
  6,
  7,
  8,
  9,
  10,
  11,
  12,
  13,
  14,
  15,
  16,
  17,
  18,
  19,
  20,
  21,
  22],
 [Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, ''),
  Text(0, 0, '')])


discussTable3 = pd.concat([total_installed3,cumu_area_deployed3_total,virgin3,wastes3,
                           cumu_e_demands3, netEnergy_cumu3.loc[2100].T, energyBalance3],
                         axis=1, keys=['replacements','area','virgin','wastes','energydemand','netenergy','energybalance'])
#discussTable.to_csv(os.path.join(testfolder,'discussiontable.csv'))
discussTable3


discussTable3_norm = (discussTable3/discussTable3.loc['PV_ICE'])*100-100
discussTable3_norm

	replacements	area	virgin	wastes	energydemand	netenergy	energybalance
PV_ICE	1.906612e+08	7.728509e+11	1.008632e+10	5.139612e+09	1.467115e+17	7.041318e+18	48.994305
life_high	1.732992e+08	7.033519e+11	9.180273e+09	4.394466e+09	1.335581e+17	7.054472e+18	53.819491
life_low	2.593475e+08	1.047702e+12	1.366772e+10	8.777769e+09	1.987961e+17	6.989234e+18	36.157805
eff_high	1.906612e+08	6.435582e+11	8.398524e+09	4.276345e+09	1.222332e+17	7.065796e+18	58.805872
eff_high_bifi	1.752295e+08	5.914535e+11	7.718548e+09	3.929078e+09	1.123348e+17	7.735658e+18	69.862551
eff_low	1.906612e+08	9.678296e+11	1.263162e+10	6.447753e+09	1.836305e+17	7.004399e+18	39.143994
circ_high	1.906612e+08	7.728509e+11	4.746508e+09	3.423740e+06	1.734309e+17	7.014599e+18	41.446065
circ_mid	1.906612e+08	7.728509e+11	8.469434e+09	3.726350e+09	1.903153e+17	6.997714e+18	37.769062
circ_low	1.906612e+08	7.728509e+11	1.011723e+10	5.374146e+09	1.441579e+17	7.043872e+18	49.862190

	replacements	area	virgin	wastes	energydemand	netenergy	energybalance
PV_ICE	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
life_high	-9.106160e+00	-8.992551	-8.982934	-14.498109	-8.965499	0.186803	9.848463
life_low	3.602537e+01	35.563336	35.507452	70.786595	35.501328	-0.739699	-26.199985
eff_high	-4.263256e-14	-16.729327	-16.733513	-16.796343	-16.684673	0.347639	20.025934
eff_high_bifi	-8.093759e+00	-23.471200	-23.475082	-23.553026	-23.431541	9.860938	42.593206
eff_low	-2.842171e-14	25.228496	25.235163	25.452119	25.164299	-0.524318	-20.105013
circ_high	0.000000e+00	0.000000	-52.941136	-99.933385	18.212201	-0.379466	-15.406363
circ_mid	0.000000e+00	0.000000	-16.030489	-27.497454	29.720734	-0.619255	-22.911321
circ_low	0.000000e+00	0.000000	0.306455	4.563261	-1.740566	0.036266	1.771398

	replacements	area	virgin	wastes	energydemand	netenergy	energybalance
PV_ICE	1.906612e+08	7.728509e+11	1.008632e+10	5.139612e+09	1.467115e+17	7.041318e+18	48.994305
good_eff_life	1.590911e+08	5.376261e+11	7.016802e+09	3.354618e+09	1.021476e+17	7.743488e+18	76.806843
good_eff_circ	1.752295e+08	5.914535e+11	6.482708e+09	2.848851e+09	1.456555e+17	7.702337e+18	53.880507
good_life_circ	1.732992e+08	7.033519e+11	7.780292e+09	3.174759e+09	1.735448e+17	7.014485e+18	41.418867
bad_life_eff	2.593475e+08	1.311428e+12	1.710881e+10	1.099588e+10	2.487427e+17	6.939287e+18	28.897452
bad_eff_circ	1.906612e+08	9.678296e+11	1.267038e+10	6.741955e+09	1.804276e+17	7.007602e+18	39.838860
bad_life_circ	2.593475e+08	1.047702e+12	1.371424e+10	9.132573e+09	1.953239e+17	6.992706e+18	36.800560

	replacements	area	virgin	wastes	energydemand	netenergy	energybalance
PV_ICE	1.906612e+08	7.728509e+11	1.008632e+10	5.139612e+09	1.467115e+17	7.041318e+18	48.994305
life_high	1.732992e+08	7.033519e+11	9.180273e+09	4.394466e+09	1.335581e+17	7.054472e+18	53.819491
life_low	2.593475e+08	1.047702e+12	1.366772e+10	8.777769e+09	1.987961e+17	6.989234e+18	36.157805
eff_high	1.906612e+08	6.435582e+11	8.398524e+09	4.276345e+09	1.222332e+17	7.065796e+18	58.805872
eff_high_bifi	1.752295e+08	5.914535e+11	7.718548e+09	3.929078e+09	1.123348e+17	7.735658e+18	69.862551
eff_low	1.906612e+08	9.678296e+11	1.263162e+10	6.447753e+09	1.836305e+17	7.004399e+18	39.143994
circ_high	1.906612e+08	7.728509e+11	4.746508e+09	3.423740e+06	1.734309e+17	7.014599e+18	41.446065
circ_mid	1.906612e+08	7.728509e+11	8.469434e+09	3.726350e+09	1.903153e+17	6.997714e+18	37.769062
circ_low	1.906612e+08	7.728509e+11	1.011723e+10	5.374146e+09	1.441579e+17	7.043872e+18	49.862190
PV_ICE	1.906612e+08	7.728509e+11	1.008632e+10	5.139612e+09	1.467115e+17	7.041318e+18	48.994305
good_eff_life	1.590911e+08	5.376261e+11	7.016802e+09	3.354618e+09	1.021476e+17	7.743488e+18	76.806843
good_eff_circ	1.752295e+08	5.914535e+11	6.482708e+09	2.848851e+09	1.456555e+17	7.702337e+18	53.880507
good_life_circ	1.732992e+08	7.033519e+11	7.780292e+09	3.174759e+09	1.735448e+17	7.014485e+18	41.418867
bad_life_eff	2.593475e+08	1.311428e+12	1.710881e+10	1.099588e+10	2.487427e+17	6.939287e+18	28.897452
bad_eff_circ	1.906612e+08	9.678296e+11	1.267038e+10	6.741955e+09	1.804276e+17	7.007602e+18	39.838860
bad_life_circ	2.593475e+08	1.047702e+12	1.371424e+10	9.132573e+09	1.953239e+17	6.992706e+18	36.800560

	replacements	area	virgin	wastes	energydemand	netenergy	energybalance
PV_ICE	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
life_high	-9.106160e+00	-8.992551	-8.982934	-14.498109	-8.965499	0.186803	9.848463
life_low	3.602537e+01	35.563336	35.507452	70.786595	35.501328	-0.739699	-26.199985
eff_high	-4.263256e-14	-16.729327	-16.733513	-16.796343	-16.684673	0.347639	20.025934
eff_high_bifi	-8.093759e+00	-23.471200	-23.475082	-23.553026	-23.431541	9.860938	42.593206
eff_low	-2.842171e-14	25.228496	25.235163	25.452119	25.164299	-0.524318	-20.105013
circ_high	0.000000e+00	0.000000	-52.941136	-99.933385	18.212201	-0.379466	-15.406363
circ_mid	0.000000e+00	0.000000	-16.030489	-27.497454	29.720734	-0.619255	-22.911321
circ_low	0.000000e+00	0.000000	0.306455	4.563261	-1.740566	0.036266	1.771398
PV_ICE	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
good_eff_life	-1.655818e+01	-30.435992	-30.432492	-34.730139	-30.375204	9.972131	56.766879
good_eff_circ	-8.093759e+00	-23.471200	-35.727718	-44.570705	-0.719795	9.387720	9.973000
good_life_circ	-9.106160e+00	-8.992551	-22.862927	-38.229599	18.289824	-0.381083	-15.461875
bad_life_eff	3.602537e+01	69.687123	69.623932	113.943791	69.545418	-1.449035	-41.018754
bad_eff_circ	-2.842171e-14	25.228496	25.619425	31.176342	22.981193	-0.478832	-18.686754
bad_life_circ	3.602537e+01	35.563336	35.968760	77.689928	33.134672	-0.690388	-24.888086

Energy Analysis Sensitivity of Lifetime, Efficiency, and Material Circularity¶

https://www.learnui.design/tools/data-color-picker.html#palette ¶

color pallette - modify here for all graphs below¶

Create Sensitivity Scenarios¶

Life¶

Efficiency¶

Material Circularity¶

Calculate Mass Flow: Identical Installs¶

Calculate Flows: Capacity Compensation¶

Fix the index to be years¶

Best Combos and Worst Combos¶

Good Combos¶

Bad Combos¶

Calculate Mass Flow: Identical Installs¶

Sensitivity of Energy to Circularity¶

	year	new_Installed_Capacity_[MW]	mod_eff	mod_reliability_t50	mod_reliability_t90	mod_degradation	mod_lifetime	mod_MFG_eff	mod_Repair	mod_MerchantTail	...	mod_EOL_pg1_landfill	mod_EOL_pg2_stored	mod_EOL_pg3_reMFG	mod_EOL_pg4_recycled	mod_EOL_reMFG_yield	mod_EOL_sp_reMFG_recycle	mod_EOL_pb1_landfill	mod_EOL_pb2_stored	mod_EOL_pb3_reMFG	mod_EOL_pb4_recycled
0	2000	808.669	13.420000	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
1	2001	284.317	13.600000	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
2	2002	344.073	13.722222	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
3	2003	527.971	13.844444	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
4	2004	1086.949	13.966667	19.0	23.0	0.75	20.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	2096	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
97	2097	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
98	2098	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
99	2099	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0
100	2100	219320.000	30.001640	40.0	44.0	0.50	35.0	98.0	0.0	0.0	...	60.0	0.0	0.0	40.0	0.0	100.0	60.0	0.0	0.0	40.0

	eff_high_mod_MFG	eff_high_mod_Install	eff_high_mod_OandM	eff_high_mod_Repair	eff_high_mod_Demount	eff_high_mod_Store	eff_high_mod_Resell_Certify	eff_high_mod_ReMFG_Disassembly	eff_high_mod_Recycle_Crush	eff_high_glass_mat_extraction	...	eff_high_bifi_copper_mat_Landfill_fuel	eff_high_bifi_copper_mat_Recycled_HQ_fuel	eff_high_bifi_encapsulant_mat_MFG_virgin_fuel	eff_high_bifi_encapsulant_mat_MFGScrap_HQ_fuel	eff_high_bifi_encapsulant_mat_Landfill_fuel	eff_high_bifi_encapsulant_mat_Recycled_HQ_fuel	eff_high_bifi_backsheet_mat_MFG_virgin_fuel	eff_high_bifi_backsheet_mat_MFGScrap_HQ_fuel	eff_high_bifi_backsheet_mat_Landfill_fuel	eff_high_bifi_backsheet_mat_Recycled_HQ_fuel
year
2000	5.595432e+10	5.724557e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	0.000000e+00	5.347604e+08	...	0.0	0.0	5.968542e+09	0.0	0.0	0.0	1.131697e+10	0.0	0.0	0.0
2001	7.599869e+10	7.752961e+07	0.0	0.0	3.657513e-01	0.0	0.0	0.0	1.547705e+00	7.242440e+08	...	0.0	0.0	8.196241e+09	0.0	0.0	0.0	1.555949e+10	0.0	0.0	0.0
2002	1.003605e+11	1.019176e+08	0.0	0.0	2.862575e+01	0.0	0.0	0.0	1.211321e+02	9.520647e+08	...	0.0	0.0	1.087936e+10	0.0	0.0	0.0	2.070531e+10	0.0	0.0	0.0
2003	1.376869e+11	1.388866e+08	0.0	0.0	3.744197e+02	0.0	0.0	0.0	1.584386e+03	1.297412e+09	...	0.0	0.0	1.502408e+10	0.0	0.0	0.0	2.872989e+10	0.0	0.0	0.0
2004	2.141538e+11	2.138242e+08	0.0	0.0	2.361542e+03	0.0	0.0	0.0	9.993052e+03	1.997442e+09	...	0.0	0.0	2.354653e+10	0.0	0.0	0.0	4.540641e+10	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2096	2.084757e+15	5.842628e+12	0.0	0.0	2.900146e+12	0.0	0.0	0.0	1.227219e+13	6.622589e+13	...	0.0	0.0	4.745288e+14	0.0	0.0	0.0	1.490753e+15	0.0	0.0	0.0
2097	2.106728e+15	5.904238e+12	0.0	0.0	2.949143e+12	0.0	0.0	0.0	1.247953e+13	6.692494e+13	...	0.0	0.0	4.796983e+14	0.0	0.0	0.0	1.506818e+15	0.0	0.0	0.0
2098	2.130099e+15	5.969776e+12	0.0	0.0	3.002774e+12	0.0	0.0	0.0	1.270647e+13	6.766857e+13	...	0.0	0.0	4.850310e+14	0.0	0.0	0.0	1.523389e+15	0.0	0.0	0.0
2099	2.154891e+15	6.039297e+12	0.0	0.0	3.061103e+12	0.0	0.0	0.0	1.295330e+13	6.845738e+13	...	0.0	0.0	4.907076e+14	0.0	0.0	0.0	1.541029e+15	0.0	0.0	0.0
2100	2.181460e+15	6.113803e+12	0.0	0.0	3.125324e+12	0.0	0.0	0.0	1.322506e+13	6.930273e+13	...	0.0	0.0	4.968002e+14	0.0	0.0	0.0	1.559962e+15	0.0	0.0	0.0

Energy Analysis Sensitivity of Lifetime, Efficiency, and Material Circularity¶

https://www.learnui.design/tools/data-color-picker.html#palette¶

color pallette - modify here for all graphs below¶

Create Sensitivity Scenarios¶

Life¶

Efficiency¶

Material Circularity¶

Calculate Mass Flow: Identical Installs¶

Calculate Flows: Capacity Compensation¶

Fix the index to be years¶

Best Combos and Worst Combos¶

Good Combos¶

Bad Combos¶

Calculate Mass Flow: Identical Installs¶

Sensitivity of Energy to Circularity¶

https://www.learnui.design/tools/data-color-picker.html#palette ¶